Patent Application
20220243378
The present subject matter relates generally to camera assemblies for use in laundry appliances, or more specifically, to the use of a camera assembly to implement a load size detection and automatic start algorithm in laundry appliances.
Washing machine appliances generally include a tub for containing water or wash fluid, e.g., water and detergent, bleach, and/or other wash additives. A basket is rotatably mounted within the tub and defines a wash chamber for receipt of articles for washing. During normal operation of such washing machine appliances, the wash fluid is directed into the tub and onto articles within the wash chamber of the basket. The basket or an agitation element can rotate at various speeds to agitate articles within the wash chamber, to wring wash fluid from articles within the wash chamber, etc. During a spin or drain cycle of a washing machine appliance, a drain pump assembly may operate to discharge water from within sump.
Operating cycles in conventional laundry appliances are initiated by a user of the appliance, e.g., the user must select the desired cycle type (e.g., cotton, mixed, etc.) and operating parameters before manually starting the wash or drying cycle by pressing a tactile “Start” button. However, consumers may commonly overfill or underfill the laundry appliance, resulting in inefficient operation. For example, a user may initiate a washing machine cycle before the basket is properly filled or may wait too long to initiate a cycle, resulting in the basket being overfilled. Moreover, a user may generally wish to have less interaction and decision-making responsibility related to cycle selection and initiation.
Accordingly, a washing machine appliance including features and operating methods for improved user interaction and wash cycle initiation would be desirable. More specifically, a method for initiating a wash cycle in an improved manner and with minimal user interaction and input would be particularly beneficial.
Advantages of the invention will be set forth in part in the following description, or may be apparent from the description, or may be learned through practice of the invention.
In one exemplary embodiment, a laundry appliance is provided including a cabinet, a basket rotatably mounted within the cabinet and defining a chamber configured for receiving a load of clothes, a camera assembly mounted within the cabinet in view of the chamber, and a controller operably coupled to the camera assembly. The controller is configured to obtain one or more images of the chamber using the camera assembly, estimate a load size of the load of clothes based at least in part on the one or more images, obtain a start size threshold, determine that the load size exceeds the start size threshold, and commence an operating cycle in response to determining that the load size exceeds the start size threshold.
In another exemplary embodiment, a method of operating a laundry appliance is provided. The laundry appliance includes a basket rotatably mounted within a cabinet and defining a chamber configured for receiving a load of clothes, and a camera assembly mounted within the cabinet in view of the chamber. The method includes obtaining one or more images of the chamber using the camera assembly, estimating a load size of the load of clothes based at least in part on the one or more images, obtaining a start size threshold, determining that the load size exceeds the start size threshold, and commencing an operating cycle in response to determining that the load size exceeds the start size threshold.
These and other features, aspects and advantages of the present invention will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.
A full and enabling disclosure of the present invention, including the best mode thereof, directed to one of ordinary skill in the art, is set forth in the specification, which makes reference to the appended figures.
Repeat use of reference characters in the present specification and drawings is intended to represent the same or analogous features or elements of the present invention.
Reference now will be made in detail to embodiments of the invention, one or more examples of which are illustrated in the drawings. Each example is provided by way of explanation of the invention, not limitation of the invention. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the scope or spirit of the invention. For instance, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that the present invention covers such modifications and variations as come within the scope of the appended claims and their equivalents.
As used herein, the terms “first,” “second,” and “third” may be used interchangeably to distinguish one component from another and are not intended to signify location or importance of the individual components. The terms “includes” and “including” are intended to be inclusive in a manner similar to the term “comprising.” Similarly, the term “or” is generally intended to be inclusive (i.e., “A or B” is intended to mean “A or B or both”). Approximating language, as used herein throughout the specification and claims, is applied to modify any quantitative representation that could permissibly vary without resulting in a change in the basic function to which it is related. Accordingly, a value modified by a term or terms, such as “about,” “approximately,” and “substantially,” are not to be limited to the precise value specified. In at least some instances, the approximating language may correspond to the precision of an instrument for measuring the value. For example, the approximating language may refer to being within a 10 percent margin.
Referring now to the figures, an exemplary laundry appliance that may be used to implement aspects of the present subject matter will be described. Specifically,
Referring to
Wash basket 120 may define one or more agitator features that extend into wash chamber 126 to assist in agitation and cleaning articles disposed within wash chamber 126 during operation of washing machine appliance 100. For example, as illustrated in
Referring generally to
A window 136 in door 134 permits viewing of wash basket 120 when door 134 is in the closed position, e.g., during operation of washing machine appliance 100. Door 134 also includes a handle (not shown) that, e.g., a user may pull when opening and closing door 134. Further, although door 134 is illustrated as mounted to front panel 130, it should be appreciated that door 134 may be mounted to another side of cabinet 102 or any other suitable support according to alternative embodiments.
Referring again to
A drain pump assembly 144 is located beneath wash tub 124 and is in fluid communication with sump 142 for periodically discharging soiled wash fluid from washing machine appliance 100. Drain pump assembly 144 may generally include a drain pump 146 which is in fluid communication with sump 142 and with an external drain 148 through a drain hose 150. During a drain cycle, drain pump 146 urges a flow of wash fluid from sump 142, through drain hose 150, and to external drain 148. More specifically, drain pump 146 includes a motor (not shown) which is energized during a drain cycle such that drain pump 146 draws wash fluid from sump 142 and urges it through drain hose 150 to external drain 148.
A spout 152 is configured for directing a flow of fluid into wash tub 124. For example, spout 152 may be in fluid communication with a water supply 154 (
As illustrated in
In addition, a water supply valve 158 may provide a flow of water from a water supply source (such as a municipal water supply 154) into detergent dispenser 156 and into wash tub 124. In this manner, water supply valve 158 may generally be operable to supply water into detergent dispenser 156 to generate a wash fluid, e.g., for use in a wash cycle, or a flow of fresh water, e.g., for a rinse cycle. It should be appreciated that water supply valve 158 may be positioned at any other suitable location within cabinet 102. In addition, although water supply valve 158 is described herein as regulating the flow of “wash fluid,” it should be appreciated that this term includes, water, detergent, other additives, or some mixture thereof.
A control panel 160 including a plurality of input selectors 162 is coupled to front panel 130. Control panel 160 and input selectors 162 collectively form a user interface input for operator selection of machine cycles and features. For example, in one embodiment, a display 164 indicates selected features, a countdown timer, and/or other items of interest to machine users. Operation of washing machine appliance 100 is controlled by a controller or processing device 166 (
Controller 166 may include a memory and microprocessor, such as a general or special purpose microprocessor operable to execute programming instructions or micro-control code associated with a cleaning cycle. The memory may represent random access memory such as DRAM, or read only memory such as ROM or FLASH. In one embodiment, the processor executes programming instructions stored in memory. The memory may be a separate component from the processor or may be included onboard within the processor. Alternatively, controller 166 may be constructed without using a microprocessor, e.g., using a combination of discrete analog and/or digital logic circuitry (such as switches, amplifiers, integrators, comparators, flip-flops, AND gates, and the like) to perform control functionality instead of relying upon software. Control panel 160 and other components of washing machine appliance 100 may be in communication with controller 166 via one or more signal lines or shared communication busses.
During operation of washing machine appliance 100, laundry items are loaded into wash basket 120 through opening 132, and washing operation is initiated through operator manipulation of input selectors 162. Wash tub 124 is filled with water, detergent, and/or other fluid additives, e.g., via spout 152 and/or detergent drawer 156. One or more valves (e.g., water supply valve 158) can be controlled by washing machine appliance 100 to provide for filling wash basket 120 to the appropriate level for the amount of articles being washed and/or rinsed. By way of example for a wash mode, once wash basket 120 is properly filled with fluid, the contents of wash basket 120 can be agitated (e.g., with ribs 128) for washing of laundry items in wash basket 120.
After the agitation phase of the wash cycle is completed, wash tub 124 can be drained. Laundry articles can then be rinsed by again adding fluid to wash tub 124, depending on the particulars of the cleaning cycle selected by a user. Ribs 128 may again provide agitation within wash basket 120. One or more spin cycles may also be used. In particular, a spin cycle may be applied after the wash cycle and/or after the rinse cycle in order to wring wash fluid from the articles being washed. During a final spin cycle, basket 120 is rotated at relatively high speeds and drain assembly 144 may discharge wash fluid from sump 142. After articles disposed in wash basket 120 are cleaned, washed, and/or rinsed, the user can remove the articles from wash basket 120, e.g., by opening door 134 and reaching into wash basket 120 through opening 132.
Referring now specifically to
Referring now briefly to
It should be appreciated that camera assembly 170 may include any suitable number, type, size, and configuration of camera(s) 178 for obtaining images of wash chamber 126. In general, cameras 178 may include a lens 182 that is constructed from a clear hydrophobic material or which may otherwise be positioned behind a hydrophobic clear lens. So positioned, camera assembly 170 may obtain one or more images or videos of clothes 172 within wash chamber 126, as described in more detail below. Referring still to
According to exemplary embodiments of the present subject matter, washing machine appliance 100 may further include a basket speed sensor 186 (
Notably, controller 166 of washing machine appliance 100 (or any other suitable dedicated controller) may be communicatively coupled to camera assembly 170, tub light 184, basket speed sensor 186, and other components of washing machine appliance 100. As explained in more detail below, controller 166 may be programmed or configured for obtaining images using camera assembly 170, e.g., in order to detect certain operating conditions and improve the performance of washing machine appliance. In addition, controller 166 may be programmed or configured to perform methods to estimate the load size of the load of clothes 172 and automatically initiate an operating cycle when desired.
Referring still to
External communication system 190 permits controller 166 of washing machine appliance 100 to communicate with external devices either directly or through a network 192. For example, a consumer may use a consumer device 194 to communicate directly with washing machine appliance 100. For example, consumer devices 194 may be in direct or indirect communication with washing machine appliance 100, e.g., directly through a local area network (LAN), Wi-Fi, Bluetooth, Zigbee, etc. or indirectly through network 192. In general, consumer device 194 may be any suitable device for providing and/or receiving communications or commands from a user. In this regard, consumer device 194 may include, for example, a personal phone, a tablet, a laptop computer, or another mobile device.
In addition, a remote server 196 may be in communication with washing machine appliance 100 and/or consumer device 194 through network 192. In this regard, for example, remote server 196 may be a cloud-based server 196, and is thus located at a distant location, such as in a separate state, country, etc. In general, communication between the remote server 196 and the client devices may be carried via a network interface using any type of wireless connection, using a variety of communication protocols (e.g. TCP/IP, HTTP, SMTP, FTP), encodings or formats (e.g. HTML, XML), and/or protection schemes (e.g. VPN, secure HTTP, SSL).
In general, network 192 can be any type of communication network. For example, network 192 can include one or more of a wireless network, a wired network, a personal area network, a local area network, a wide area network, the internet, a cellular network, etc. According to an exemplary embodiment, consumer device 194 may communicate with a remote server 196 over network 192, such as the internet, to provide user inputs, transfer operating parameters or performance characteristics, receive user notifications or instructions, etc. In addition, consumer device 194 and remote server 196 may communicate with washing machine appliance 100 to communicate similar information.
External communication system 190 is described herein according to an exemplary embodiment of the present subject matter. However, it should be appreciated that the exemplary functions and configurations of external communication system 190 provided herein are used only as examples to facilitate description of aspects of the present subject matter. System configurations may vary, other communication devices may be used to communicate directly or indirectly with one or more laundry appliances, other communication protocols and steps may be implemented, etc. These variations and modifications are contemplated as within the scope of the present subject matter.
While described in the context of a specific embodiment of horizontal axis washing machine appliance 100, using the teachings disclosed herein it will be understood that horizontal axis washing machine appliance 100 is provided by way of example only. Other washing machine appliances having different configurations, different appearances, and/or different features may also be utilized with the present subject matter as well, e.g., vertical axis washing machine appliances. In addition, aspects of the present subject matter may be utilized in a combination washer/dryer appliance. Indeed, it should be appreciated that aspects of the present subject matter may further apply to other laundry appliances, such a dryer appliance. In this regard, the same methods and systems as described herein may be used to estimate load size and automatically start an operating cycle in other appliances, such as a dryer.
Now that the construction of washing machine appliance 100 and the configuration of controller 166 according to exemplary embodiments have been presented, an exemplary method 200 of operating a washing machine appliance will be described. Although the discussion below refers to the exemplary method 200 of operating washing machine appliance 100, one skilled in the art will appreciate that the exemplary method 200 is applicable to the operation of a variety of other washing machine appliances, such as vertical axis washing machine appliances. In exemplary embodiments, the various method steps as disclosed herein may be performed by controller 166 or a separate, dedicated controller.
Referring now to
Continuing the example from above, camera assembly 170 may be used to obtain images within wash basket 120 as clothes are being added to wash chamber 126, thereby obtaining useful information related to the load which may be utilized for improved appliance performance. In general, step 210 includes obtaining one or more images, a series of images/frames, or a video of wash chamber 126. Step 210 may further include taking a still image from the video clip or otherwise obtaining a still representation or photo from the video clip. It should be appreciated that the images obtained by camera assembly 170 may vary in number, frequency, angle, resolution, detail, etc. In addition, according to exemplary embodiments, controller 166 may be configured for illuminating the tub using tub light 184 just prior to obtaining images. In this manner, by ensuring wash chamber 126 is well illuminated, camera assembly 170 should be capable of obtaining a clear image of wash chamber 126. Notably, as explained below, the images obtained at step 210 are used to detect a load size or other useful information related to the load of clothes 172 within wash chamber 126 according to exemplary embodiments of the present subject matter.
Referring again to
Each of these image evaluation processes will be described below according to exemplary embodiments of the present subject matter. It should be appreciated that image processing and machine learning image recognition processes may be used together to extract detailed information regarding the load of clothes present within wash chamber 126 to facilitate improved appliance operation. According to exemplary embodiments, any suitable number and combination of image processing or analysis techniques may be used to obtain an accurate estimation of load size, load type, etc.
As used herein, the term “image processing algorithm” and the like is generally intended to refer to any suitable methods or algorithms for analyzing images of wash chamber 126 that do not rely on artificial intelligence or machine learning techniques (e.g., in contrast to the machine learning image recognition process is described below). For example, the image processing algorithm may rely on image differentiation, e.g., such as a pixel-by-pixel comparison of two sequential images. For example, the images obtained at step 210 may be compared to a series of images of a wash chamber with a known quantity of clothes. By comparing differences between the actual obtained images of the wash chamber to images with known load quantities, controller 166 may determine which image is most similar and may deduce the load size accordingly.
As noted above, step 220 may further include estimating a load size using a machine learning image recognition process. In this regard, the images obtained at step 210 may be used by controller 166 for estimating load size within wash chamber 126. In addition, it should be appreciated that this image analysis or processing may be performed locally (e.g., by controller 166) or remotely (e.g., by a remote server 196). According to exemplary embodiments of the present subject matter, step 220 may include analyzing the images of the wash chamber using a neural network classification module and/or a machine learning image recognition process. In this regard, for example, controller 166 may be programmed to implement the machine learning image recognition process that includes a neural network trained with a plurality of images of wash chambers having a load of clothes with a particular load size, load type, load characteristic, etc. By analyzing the images obtained at step 210 using this machine learning image recognition process, controller 166 may estimate a load size within wash basket 120, along with other useful qualitative or quantitative information.
As used herein, the terms image recognition process and similar terms may be used generally to refer to any suitable method of observation, analysis, image decomposition, feature extraction, image classification, etc. of one or more images or videos taken within a wash chamber of a washing machine appliance. In this regard, the image recognition process may use any suitable artificial intelligence (AI) technique, for example, any suitable machine learning technique, or for example, any suitable deep learning technique. It should be appreciated that any suitable image recognition software or process may be used to analyze images taken by camera assembly 170 and controller 166 may be programmed to perform such processes and take corrective action.
According to an exemplary embodiment, controller may implement a form of image recognition called region based convolutional neural network (“R-CNN”) image recognition. Generally speaking, R-CNN may include taking an input image and extracting region proposals that include a potential object, such as a particular garment, region of a load of clothes, etc. In this regard, a “region proposal” may be regions in an image that could belong to a particular object, such as a garment or load of clothes within the wash basket. A convolutional neural network is then used to compute features from the regions proposals and the extracted features will then be used to determine a classification for each particular region.
According to still other embodiments, an image segmentation process may be used along with the R-CNN image recognition. In general, image segmentation creates a pixel-based mask for each object in an image and provides a more detailed or granular understanding of the various objects within a given image. In this regard, instead of processing an entire image—i.e., a large collection of pixels, many of which might not contain useful information—image segmentation may involve dividing an image into segments (e.g., into groups of pixels containing similar attributes) that may be analyzed independently or in parallel to obtain a more detailed representation of the object or objects in an image. This may be referred to herein as “mask R-CNN” and the like.
According to still other embodiments, the image recognition process may use any other suitable neural network process. For example, step 220 may include using Mask R-CNN instead of a regular R-CNN architecture. In this regard, Mask R-CNN is based on Fast R-CNN which is slightly different than R-CNN. For example, R-CNN first applies CNN and then allocates it to zone recommendations on the covn5 property map instead of the initially split into zone recommendations. In addition, according to exemplary embodiments standard CNN may be used to analyze the image and estimate load size within wash basket 120. In addition, a K-means algorithm may be used. Other image recognition processes are possible and within the scope of the present subject matter.
It should be appreciated that any other suitable image recognition process may be used while remaining within the scope of the present subject matter. For example, step 220 may include using a deep belief network (“DBN”) image recognition process. A DBN image recognition process may generally include stacking many individual unsupervised networks that use each network's hidden layer as the input for the next layer. According to still other embodiments, step 220 may include the implementation of a deep neural network (“DNN”) image recognition process, which generally includes the use of a neural network (computing systems inspired by the biological neural networks) with multiple layers between input and output. Other suitable image recognition processes, neural network processes, artificial intelligence (“AI”) analysis techniques, and combinations of the above described or other known methods may be used while remaining within the scope of the present subject matter.
Step 230 may include obtaining a start size threshold. As used herein, the “start size threshold” is generally intended to refer to a target load size at which an operating cycle of a laundry appliance or washing machine appliance 100 is to be commenced. This start size threshold may be preprogrammed into controller 166, may be set by a user in any suitable manner, may be determined based on the load type, or may be determined or calculated in any other suitable manner. For example, according to an exemplary embodiment, a user may set a preferred start size threshold using control panel 160 or via remote device 194 (e.g., by a software application on a user's mobile phone). For example, referring now briefly to
Step 240 includes determining that the load size exceeds the start size threshold. In this manner, for example, if a user selects a start size threshold of 40% full load, the user may continuously add clothes as they become dirty while controller 166 may constantly monitor the load size within wash chamber 126. Once the measured load size exceeds the start size threshold (e.g., as selected in step 230), controller 166 may initiate the wash cycle, as described in more detail below. Notably, it is further desirable that a door the laundry appliance is closed before initiating an operating cycle. Therefore, step 250 may include determining that a door of the laundry appliance is closed, e.g., by monitoring a door switch or using any other means for detecting the position of the door.
Step 260 includes commencing an operating cycle in response to determining that the load size exceeds the start size threshold and the door is closed. In this manner, method 200 eliminates additional steps typically required by a user when initiating an operating cycle of a laundry appliance. For example, a user of a laundry appliance implementing method 200 will no longer need to push a button to wake up the appliance, will not need to expend effort determining when a laundry cycle should be run, will not need to have knowledge of the optimum time to run an operating cycle, and will not need to push the start button to commence such a cycle.
Step 270 may further include providing a user notification that the load size exceeds the start size threshold and that the operating cycle is ready for commencement or has been commenced. It should be appreciated that the user notification is optional and may be provided to the user from any suitable source and in any suitable manner. For example, according to exemplary embodiments, the user notification may be provided through control panel 160 so that the user may be aware of the operating cycle. In addition, or alternatively, controller 166 may be configured to provide a user notification to a remote device, such as remote device 194 via a network 192. Whether provided via control panel 160, remote device 194, or by other means, this user notification may include useful regarding the load size, load type, or other load characteristics. In addition, this user notification may include useful information regarding the optimized cycle parameters, the estimated finish time, or other useful information.
For example, according to an exemplary embodiment, method 200 may further include determining a remaining cycle time based at least in part on the load size estimation (e.g., from step 220). In this regard, based on the estimated load size and optimized cycle parameters, controller 166 of washing machine appliance 100 may determine with a good degree of accuracy how long the operating cycle should take. Controller 166 may then communicate that remaining cycle time to a user of the appliance, e.g., via control panel 160, remote device 194, or any other suitable device.
Referring now briefly to
According to exemplary embodiments, method 300 may further include, at step 306, a load type detection process. According to exemplary embodiments, this load type process detection may also rely on image processing or analysis techniques as described herein in order to determine the type of load size (e.g., delicates, cottons, mixed load, towels, etc.). Notably, this information may be useful in determining the estimated load time or otherwise optimizing cycle parameters. Step 308 may include updating the estimated cycle time to provide a live estimate to a user of the appliance. For example, controller 166 may be configured for updating and communicating an estimated remaining cycle time, e.g., based at least in part on the load size and/or load type determined at steps 304 and 306.
Step 310 includes determining whether the load size (e.g., as measured at step 304) is greater than a user selected load size start threshold. If the load size is still below the start threshold, step 312 includes not starting the cycle and waiting until more clothes are added and the process continues until the threshold is reached. By contrast, if the load size exceeds the start threshold, step 314 may include notifying a user that the load size has reached the threshold in that an operating cycle is about to commence. This notification may be achieved via an LED indicator on the laundry appliance, via a push notification to a remote device, via control panel 160, or in any other suitable manner. Step 316 includes a process for confirming that the door is closed prior to initiating the operating cycle.
According to exemplary embodiments, method 300 may further include additional child safety features, such as implementation of a child safety algorithm at step 318. Step 320 may include optimizing cycle parameters and initiating the operating cycle. For example, washing machine appliance 100 may optimize detergent dosage, water level, agitation profile, spin profile, or other operating characteristics based at least in part on the load size determined at step 304, the flow type detected at step 306, or any other useful information or user selected parameters.
This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they include structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.
